1. Field of the Invention
The present invention relates, in general, to neural networks and, more particularly, to neural networks incorporated into sedation and analgesia systems.
2. Description of the Related Art
A sedation and analgesia system has been developed to provide patients undergoing painful, uncomfortable or otherwise frightening (anxiety inspiring) medical or surgical procedures with a means for receiving sedative, analgesic, and/or amnestic drugs safely in a way that reduces the risk of overmedication with or without the presence of a licensed anesthesia provider. Due to significant advances in technology, the sedation and analgesia system may be safer for use in hospital and ambulatory environments and may be operated by individuals other than trained anesthesiologists such as, for example, C.R.N.A.s, trained physicians, or other trained operators. The sedation and analgesia system has gone far to meet the needs of practitioners who are unable to schedule anesthesia providers for every procedure where safe and effective sedation and analgesia could substantially mitigate the effects of fear and pain. The advent of a sedation and analgesia system devoted to these purposes provides these individuals with a drug delivery system integrated into a patient monitoring system that decreases the cognitive and manual workload required with the operation of anesthesia machines, yet keeps the clinician in the loop of patient management. The clinician maintains ultimate decision making responsibility following a “clinician knows best” philosophy. This advanced technology allows for the sedation and analgesia system to be operated at drug level effects less than general anesthesia without an anesthesia provider, providing the patient with a cost-effective and readily available means of sedation, amnesia, and/or analgesia.
An example of a sedation and analgesia system is described in U.S. patent application Ser. No. 09/324,759, filed Jun. 3, 1999 and incorporated herein by reference in its entirety. This sedation and analgesia system electronically integrates, for example, the delivery of one or more sedative, analgesic, and/or amnestic drugs, the delivery of positive airway pressure, decreases or increases in drug delivery, the delivery of oxygen, changes in drugs to, for example, an opioid antagonist, requests for additional information from patient monitors, and the triggering of alarms, with the electronic monitoring of one or more patient physiological conditions. In one form, the system of the ‘759 application uses one or more sets of stored data-defining parameters reflecting patient and system states, the parameters being accessed through software to conservatively manage and correlate drug delivery to safe, cost effective, optimized values related to the conscious patient's vital signs and other physiological conditions.
As the computational and decision making capabilities of such integrated monitoring and drug delivery systems are escalated, accurate data analysis plays an increasingly crucial role in ensuring patient safety. Often, such systems are programmed to monitor increasingly complex data that may be prone to ambiguity or artifact with traditional Von Neumann machines. In response to this, software development has largely been slowed in areas such as waveform monitoring where extensive time and money is spent on developing sophisticated algorithms that may still not operate satisfactorily.
A further problem with conventional approaches to handling complex data is that they are frequently computationally expensive which either necessitates enormous computing power, or simply precludes their use in systems which must perform in real-time. For example, in the analysis of physiological waveforms, real time performance is often essential to achieve results fast enough to take corrective responses to abnormalities in a patient's physiological condition.
Neural network architectures, which are loosely based on knowledge of the neuroanatomy of the brain, have been shown to perform well at tasks such as the classification of waveforms having subtle differences-tasks which heretofore have been limited to performance by humans. In addition to their robust ability to recognize characteristic waveforms which vary widely from predicted shapes, neural networks may offer solutions to other aspects of data analysis in sedation and analgesia systems. Neural networks need not require explicit algorithms to analyze the large and potentially spurious and ambiguous data created by comprehensive patient monitoring. Instead, these systems, trained with exemplars, converge to an acceptable solution. In addition, once trained, a neural network can generally perform a recognition task rapidly due to its inherent parallelism.